Efficient data retrieval in big-data processing systems

ABSTRACT

A mechanism is provided for implementing operation optimization. Responsive to a request to load data via a input/output (I/O) load operation, prior to loading the data, transformed data in a local memory is searched for a match to the requested data. Responsive to identifying transformed that matches the requested data, the transformed data in the local memory is used to fulfill the request. Responsive to failing to identify transformed data in the local memory that matches the requested data, untransformed data in the local memory is searched for a match to the requested data. Responsive to identifying untransformed data that matches the requested data, the untransformed data in the local memory is used to fulfill the request. Responsive to failing to identify untransformed data in the local memory that matches the requested data, the requested data may be loaded from the storage system via the I/O load operation.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for efficientdata retrieval in big-data processing systems.

Big data is a term for data sets that are so large or complex thattraditional data processing applications are inadequate to deal withthem. Challenges include analysis, capture, data uration, search,sharing, storage, transfer, visualization, querying, updating, andinformation privacy. The term “big data” often refers simply to the useof predictive analytics, user behavior analytics, or certain otheradvanced data analytics methods that extract value from data, and seldomto a particular size of data set.

Analysis of data sets may find new correlations to “spot businesstrends, prevent diseases, combat crime, and so on”. Scientists, businessexecutives, practitioners of medicine, advertising, and governmentsalike regularly meet difficulties with large data sets in areasincluding Internet search, finance, urban informatics, and businessinformatics. Scientists encounter limitations in e-Science work,including meteorology, genomics, connectomics, complex physicssimulations, biology, and environmental research. Massive open onlinecourses (MOOCs) also bring big-data challenges as the courses reuse thesame data sets for the students projects.

Big-data processing systems analyze big-data sets at terabyte or evenpetabyte scale. Offline batch data processing is typically full powerand full scale, tackling arbitrary time series fact use cases. Whilereal-time stream processing is performed on the most current slice ofdata for data profiling to pick outliers, fraud transaction detections,security monitoring, etc., the toughest task however is to do fast (lowlatency) or real-time ad-hoc analytics on a complete big data set, whichpractically means that terabytes (or even more) of data has to bescanned within seconds. This is only possible when data is processedwith high parallelism, such as that used in big-data processing systems.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method, in a data processing system,is provided for implementing operation optimization in the dataprocessing system. The illustrative embodiment, prior to loading thedata from the storage system, searches transformed data in a localmemory to identify data that matches the requested data in response to arequest from an application to load data from a storage system via ainput/output (I/O) load operation. The illustrative embodiment uses thetransformed data in the local memory to fulfill the request in responseto identifying transformed data in the local memory that matches therequested data. The illustrative embodiment search untransformed data inthe local memory to identify data that matches the requested data inresponse to failing to identify transformed data in the local memorythat matches the requested data. The illustrative embodiment uses theuntransformed data in the local memory to fulfill the request inresponse to identifying untransformed data in the local memory thatmatches the requested data. The illustrative embodiment loads therequested data from the storage system via the I/O load operation inresponse to failing to identify untransformed data in the local memorythat matches the requested data.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is an example diagram of a distributed data processing system inwhich aspects of the illustrative embodiments may be implemented;

FIG. 2 is an example block diagram of a computing device in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 depicts a functional block diagram of an operation optimizationmechanism that optimizes I/O load operations in accordance with anillustrative embodiment; and

FIGS. 4A and 4B depict an exemplary flowchart of the operationsperformed by an operation optimization mechanism that optimizes I/O loadoperations in accordance with an illustrative embodiment.

DETAILED DESCRIPTION

Modern big-data processing systems are designed for bio-statisticianswho are not experts in system usage optimization. In order to avoidcostly big-data input/output (I/O) processing operations, the big-dataprocessing systems keep as much data in memory as possible. To do this,the big-data processing systems offer various features to users, e.g.,allowing data be kept in memory. However, when loading additionaldatasets or transforming similar datasets, data already existing inmemory is not typically considered by the regular user. This may lead toexpensive, redundant I/O operations as data already existing in memoryis loaded in to memory again. For example, loading a dataset and thenloading that same dataset again causes I/O operations to occur bothtimes despite the fact that the data is already loaded into memory.Prior attempts to solve such issues provide simple solutions focused onblock level I/O optimization. However, these approaches lack visibilityinto the user application space ignoring a rich source of large I/Ooptimizations, thus missing significant potential for further I/Ooptimization.

The illustrative embodiments provide an operation optimization mechanismthat, responsive to a request from an application to load data from astorage system via a input/output (I/O) load operation, prior to loadingthe requested data from the storage system, searches transformed data ina local memory to identify data that matches the requested data. If theoperation optimization mechanism identifies transformed data in thelocal memory that matches the requested data, the operation optimizationmechanism uses the transformed data in the local memory to fulfill therequest. If the operation optimization mechanism fails to identifytransformed data in the local memory that matches the requested data,the operation optimization mechanism searches untransformed data in thelocal memory to identify data that matches the requested data. If theoperation optimization mechanism identifies untransformed data in thelocal memory that matches the requested data, the operation optimizationmechanism uses the untransformed data in the local memory to fulfill therequest. If the operation optimization mechanism fails to identifyuntransformed data in the local memory that matches the requested data,the operation optimization mechanism loads the requested data from thestorage system via the I/O load operation.

In accordance with the illustrative embodiments, data in a local memorymay be visible to the (a) threads and (b) processes on the virtualmachines associated with the threads.

Additionally, the operation optimization mechanism may determine whethera portion of data in the local memory is being utilized by more than oneapplication. If the operation optimization mechanism determines that theportion of data in the local memory is being utilized by more than oneapplication, the operation optimization mechanism marks the portion ofdata such that the portion of data is persisted within the local memoryfor a longer time than unmarked portions of data.

In another embodiment, responsive to the operation optimizationmechanism failing to identify untransformed data in the local memorythat matches the requested data and prior to loading the requested datafrom the storage system, the operation optimization mechanism searchestransformed data in a shared memory to identify data that matches therequested data. If the operation optimization mechanism identifiestransformed data in the shared memory that matches the requested data,the operation optimization mechanism uses the transformed data in theshared memory to fulfill the request. If the operation optimizationmechanism fails to identify transformed data in the shared memory thatmatches the requested data, the operation optimization mechanismsearches untransformed data in the shared memory to identify data thatmatches the requested data. If the operation optimization mechanismidentifies untransformed data in the shared memory that matches therequested data, the operation optimization mechanism uses theuntransformed data in the shared memory to fulfill the request. If theoperation optimization mechanism fails to identify untransformed data inthe shared memory that matches the requested data, the operationoptimization mechanism loads the requested data from the storage systemvia the I/O load operation.

In accordance with the illustrative embodiments, data in a shared memoryblock is visible to all threads sharing that block. These threads may beallocated to multiple processing systems within a host system and mayaccess that block for the lifetime of the block. This is invaluablebecause shared memory allows for threads of different processing systemsto communicate and share data between one another.

In order for this second embodiment to operate, the operationoptimization mechanism may determine whether a portion of data in thelocal memory is being utilized by multiple processing systems eachrunning an application, i.e. multiple threads, in the data processingsystem above a predetermined frequency threshold. If the operationoptimization mechanism determines that the portion of data in the localmemory is being utilized by multiple processing systems each running anapplication in the data processing system above the predeterminedfrequency threshold, the operation optimization mechanism moves theportion of data from the local memory to a shared memory, the sharedmemory being accessible by the multiple processing systems. Thus, theoperation optimization mechanism is then able to redirect accesses tothe portion of data to the shared memory.

Similarly, the operation optimization mechanism may determine whether adata stream is being utilized by multiple processing systems eachrunning an application, i.e. multiple threads, in the data processingsystem. If the operation optimization mechanism determines that the datastream is being utilized by multiple processing systems each running anapplication in the data processing system, the operation optimizationmechanism loads the data stream into a shared memory, the shared memorybeing accessible by the multiple processing systems. Thus, the operationoptimization mechanism is then able to redirect accesses to the datastream to the shared memory. The operation optimization mechanism isable to identify the data stream to be utilized by the multipleprocessing systems by performing a static code analysis of eachapplication running on each of the multiple processing systems toidentify one or more data streams to be utilized and, responsive to twoor more applications utilizing a same data stream, identify the datastream as the data stream to be utilized by the multiple processingsystems.

In yet another embodiment, responsive to the operation optimizationmechanism failing to identify untransformed data in the shared memorythat matches the requested data and prior to loading the requested datafrom the storage system, the operation optimization mechanism searchestransformed data in a global memory to identify data that matches therequested data. If the operation optimization mechanism identifiestransformed data in the global memory that matches the requested data,the operation optimization mechanism uses the transformed data in theglobal memory to fulfill the request. If the operation optimizationmechanism fails to identify transformed data in the global memory thatmatches the requested data, the operation optimization mechanismsearches untransformed data in the global memory to identify data thatmatches the requested data. If the operation optimization mechanismidentifies untransformed data in the global memory that matches therequested data, the operation optimization mechanism uses theuntransformed data in the global memory to fulfill the request. If theoperation optimization mechanism fails to identify untransformed data inthe global memory that matches the requested data, the operationoptimization mechanism loads the requested data from the storage systemvia the I/O load operation.

In accordance with the illustrative embodiments, data in a global memorymay be visible (depending on the access control) to all threads withinall blocks (including all virtual machines), and the duration of theglobal memory is independent of the lifecycle of virtual machineallocation and threads duration. An example of such global memory isnetwork-attached storage (NAS), Cloud Databases, Apache Ignite, or thelike.

Before beginning the discussion of the various aspects of theillustrative embodiments, it should first be appreciated thatthroughout, this description the term “mechanism” will be used to referto elements of the present invention that perform various operations,functions, and the like. A “mechanism,” as the term is used herein, maybe an implementation of the functions or aspects of the illustrativeembodiments in the form of an apparatus, a procedure, or a computerprogram product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general-purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general-purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” ifused herein with regard to describing embodiments and features of theinvention, is not intended to be limiting of any particularimplementation for accomplishing and/or performing the actions, steps,processes, etc., attributable to and/or performed by the engine. Anengine may be, but is not limited to, software, hardware and/or firmwareor any combination thereof that performs the specified functionsincluding, but not limited to, any use of a general and/or specializedprocessor in combination with appropriate software loaded or stored in amachine readable memory and executed by the processor. Further, any nameassociated with a particular engine is, unless otherwise specified, forpurposes of convenience of reference and not intended to be limiting toa specific implementation. Additionally, any functionality attributed toan engine may be equally performed by multiple engines, incorporatedinto and/or combined with the functionality of another engine of thesame or different type, or distributed across one or more engines ofvarious configurations.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

Thus, the illustrative embodiments may be utilized in many differenttypes of data processing environments. In order to provide a context forthe description of the specific elements and functionality of theillustrative embodiments, FIGS. 1 and 2 are provided hereafter asexample environments in which aspects of the illustrative embodimentsmay be implemented. It should be appreciated that FIGS. 1 and 2 are onlyexamples and are not intended to assert or imply any limitation withregard to the environments in which aspects or embodiments of thepresent invention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

FIG. 1 depicts a pictorial representation of an example distributed dataprocessing system in which aspects of the illustrative embodiments maybe implemented. Distributed data processing system 100 may include anetwork of computers in which aspects of the illustrative embodimentsmay be implemented. The distributed data processing system 100 containsat least one network 102, which is the medium used to providecommunication links between various devices and computers connectedtogether within distributed data processing system 100. The network 102may include connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server 104 and server 106 are connected tonetwork 102 along with storage unit 108. In addition, clients 110, 112,and 114 are also connected to network 102. These clients 110, 112, and114 may be, for example, personal computers, network computers, or thelike. In the depicted example, server 104 provides data, such as bootfiles, operating system images, and applications to the clients 110,112, and 114. Clients 110, 112, and 114 are clients to server 104 in thedepicted example, Distributed data processing system 100 may includeadditional servers, clients, and other devices not shown.

In the depicted example, distributed data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, the distributed data processing system 100 may also beimplemented to include a number of different types of networks, such asfor example, an intranet, a local area network (LAN), a wide areanetwork (WAN), or the like. As stated above, FIG. 1 is intended as anexample, not as an architectural limitation for different embodiments ofthe present invention, and therefore, the particular elements shown inFIG. 1 should not be considered limiting with regard to the environmentsin which the illustrative embodiments of the present invention may beimplemented.

As shown in FIG. 1, one or more of the computing devices, e.g., server104, may be specifically configured to implement an operationoptimization mechanism optimizes I/O load operations. The configuring ofthe computing device may comprise the providing of application specifichardware, firmware, or the like to facilitate the performance of theoperations and generation of the outputs described herein with regard tothe illustrative embodiments. The configuring of the computing devicemay also, or alternatively, comprise the providing of softwareapplications stored in one or more storage devices and loaded intomemory of a computing device, such as server 104, for causing one ormore hardware processors of the computing device to execute the softwareapplications that configure the processors to perform the operations andgenerate the outputs described herein with regard to the illustrativeembodiments. Moreover, any combination of application specific hardware,firmware, software applications executed on hardware, or the like, maybe used without departing from the spirit and scope of the illustrativeembodiments.

It should be appreciated that once the computing device is configured inone of these ways, the computing device becomes a specialized computingdevice specifically configured to implement the mechanisms of theillustrative embodiments and is not a general-purpose computing device.Moreover, as described hereafter, the implementation of the mechanismsof the illustrative embodiments improves the functionality of thecomputing device and provides a useful and concrete result thatfacilitates optimization of I/O load operations.

As noted above, the mechanisms of the illustrative embodiments utilizespecifically configured computing devices, or data processing systems,to perform the operations for optimizing I/O load operations. Thesecomputing devices, or data processing systems, may comprise varioushardware elements that are specifically configured, either throughhardware configuration, software configuration, or a combination ofhardware and software configuration, to implement one or more of thesystems/subsystems described herein. FIG. 2 is a block diagram of justone example data processing system in which aspects of the illustrativeembodiments may be implemented. Data processing system 200 is an exampleof a computer, such as server 104 in FIG. 1, in which computer usablecode or instructions implementing the processes and aspects of theillustrative embodiments of the present invention may be located and/orexecuted so as to achieve the operation, output, and external effects ofthe illustrative embodiments as described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit(s) 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202, Graphics processor 210 may be connected toNB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCl/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240, PCl/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface, Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit(s) 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBMeServer™ System P® computer system, Power™ processor based computersystem, or the like, running the Advanced interactive Executive (AIX®)operating system or the LINUX® operating system. Data processing system200 may be a symmetric multiprocessor (SMP) system including a pluralityof processors in processing unit(s) 206. Alternatively, a singleprocessor system may be employed.

Instructions and data for the operating system, the object-orientedprogramming system, and applications or programs are located on storagedevices, such as HDD 226, and may be loaded into main memory 208 forexecution by processing unit(s) 206. The processes for illustrativeembodiments of the present invention may be performed by processingunit(s) 206 using computer usable program code and data, which may belocated in a memory such as, for example, main memory 208, ROM 224, orin one or more peripheral devices 226 and 230, for example. Accordingly,main memory 208 may comprise one or more of local memory, shared memory,and global memory. Local memory comprises data that may be visible tothe (a) threads and (b) processes on the virtual machines associatedwith the threads. Shared memory comprises data that may be visible toall threads sharing that block. These threads may be allocated tomultiple processing systems within a host system and may access thatblock for the lifetime of the block. This is invaluable because sharedmemory allows for threads of different processing systems to communicateand share data between one another. Global memory comprises data thatmay be visible (depending on the access control) to all threads withinall blocks (including all virtual machines), and the duration of theglobal memory is independent of the lifecycle of virtual machineallocation and threads duration. An example of such global memory isnetwork-attached storage (NAS), Cloud Databases, Apache Ignite, or thelike.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

As mentioned above, in some illustrative embodiments the mechanisms ofthe illustrative embodiments may be implemented as application specifichardware, firmware, or the like, application software stored in astorage device, such as HDD 226 and loaded into memory, such as mainmemory 208, for executed by one or more hardware processors, such asprocessing unit(s) 206, or the like. As such, the computing device shownin FIG. 2 becomes specifically configured to implement the mechanisms ofthe illustrative embodiments and specifically configured to perform theoperations and generate the outputs described hereafter with regard tothe optimizing I/O load operations.

Those of ordinary skill in the art will appreciate that the hardware inFIGS. 1 and 2 may vary depending on the implementation. Other internalhardware or peripheral devices, such as flash memory, equivalentnon-volatile memory, or optical disk drives and the like, may be used inaddition to or in place of the hardware depicted in FIGS. 1 and 2. Also,the processes of the illustrative embodiments may be applied to amultiprocessor data processing system, other than the SMP systemmentioned previously, without departing from the spirit and scope of thepresent invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 depicts a functional block diagram of an operation optimizationmechanism that optimizes I/O load operations in accordance with anillustrative embodiment. Data processing system 300, which is a dataprocessing system such as data processing system 200 of FIG. 2,comprises operation optimization mechanism 302, which is coupled toprocessors 304, main memory 306, and storage system 308. Main memory 306further comprises local memory 310, shared memory 312, and global memory314. In operation, operation optimization mechanism 302 receives arequest from an application via one of processors 304 to load data fromstorage system 308 via an input/output (I/O) load operation. Prior toloading the requested data via the I/O load operation from storagesystem 308, operation optimization mechanism 302 searches transformeddata in local memory 310 to identify data that matches the requesteddata. Transformed data is data that has already been loaded from storagesystem 308 into local memory 310 and has been changed by any operationperformed by processors 304 for the application. That is, untransformeddata was loaded via an I/O operation from storage system 308 to localmemory 310 for use by the application and was changed to transformeddata by processor 304 due to a requested operation by the application.

If operation optimization mechanism 302 identifies transformed data inlocal memory 310 that matches the requested data, operation optimizationmechanism 302 uses the transformed data in local memory 310 to fulfillthe request. If operation optimization mechanism 302 fails to identifytransformed data in local memory 310 that matches the requested data,operation optimization mechanism 302 searches untransformed data inlocal memory 310 to identify data that matches the requested data.Untransformed data is data that has already been loaded from storagesystem 308 into local memory 310 but has not been changed by anyoperation performed by processors 304 for the application. That is, theuntransformed data was loaded via an I/O operation from storage system308 to local memory 310 for use by the application but the untransformeddata was not changed by processor 304 due to a requested operation bythe application.

If operation optimization mechanism 302 identifies untransformed data inlocal memory 310 that matches the requested data, operation optimizationmechanism 302 uses the untransformed data in local memory 310 to fulfillthe request. If operation optimization mechanism 302 fails to identifyuntransformed data in local memory 310 that matches the requested data,in one embodiment, operation optimization mechanism 302 may load therequested data from storage system 308 via the I/O load operation.

While data usually resides in local memory 310 only for the lifetime ofthe thread that requested the data or wrote to the data, in order tominimize I/O operations to storage system 308, operation optimizationmechanism 302 may determine whether a portion of data in local memory310 is being utilized by more than one application. If operationoptimization mechanism 302 determines that the portion of data in localmemory 310 is being utilized by more than one application, operationoptimization mechanism 302 marks the portion of data such that theportion of data is persisted within local memory 310 for a longer timethan unmarked portions of data in local memory 310.

In another embodiment, rather than loading the requested data fromstorage system 308 via the I/O load operation in response to failing toidentify untransformed data in local memory 310 that matches therequested data, operation optimization mechanism 302 may, prior toloading the requested data from storage system 308, search transformeddata in shared memory 312 to identify data that matches the requesteddata. Shared memory 312 comprises data that is visible to all threadswithin a block allocated to multiple ones of processors 304 and lastsfor the duration of the block. For data to be located in shared memory312, operation optimization mechanism 302 determines whether a portionof data in local memory 310 is being utilized by multiple one ofprocessors 304 each running at least one application, i.e. multiplethreads in a same block, in data processing system 300 above apredetermined frequency threshold. If operation optimization mechanism302 determines that the portion of data in local memory 310 is beingutilized by multiple ones of processors 304 each running at least oneapplication in data processing system 300 above the predeterminedfrequency threshold, operation optimization mechanism 302 moves theportion of data from local memory 310 to shared memory 312, sharedmemory 312 being accessible by multiple ones of processors 304. Thus,operation optimization mechanism 302 is then able to redirect accessesto the portion of data to shared memory 312.

Similarly, operation optimization mechanism 302 may determine whether adata stream is being utilized by multiple ones of processors 304 eachrunning at least one application, i.e. multiple threads, in dataprocessing system 300. If operation optimization mechanism 302determines that the data stream is being utilized by multiple ones ofprocessors 304 each running at least one application in data processingsystem 300, operation optimization mechanism 302 loads the data streaminto shared memory 312. Thus, operation optimization mechanism 302 isthen able to redirect accesses from multiple ones of processors 304 tothe data stream to shared memory 312. Operation optimization mechanism302 is able to identify the data stream to be utilized by the multipleones of processors 304 by performing a static code analysis of eachapplication running on each of the multiple ones of processors 304 toidentify one or more data streams to be utilized and, responsive to twoor more applications utilizing a same data stream, identify the datastream as the data stream to be utilized by the multiple ones ofprocessors 304.

Returning to the previous operation, if operation optimization mechanism302 identifies transformed data in shared memory 312 that matches therequested data, operation optimization mechanism 302 uses thetransformed data in shared memory 312 to fulfill the request. Ifoperation optimization mechanism 302 fails to identify transformed datain shared memory 312 that matches the requested data, operationoptimization mechanism 302 searches untransformed data in shared memory312 to identify data that matches the requested data. If operationoptimization mechanism 302 identifies untransformed data in sharedmemory 312 that matches the requested data, operation optimizationmechanism 302 uses the untransformed data in shared memory 312 tofulfill the request. If operation optimization mechanism 302 fails toidentify untransformed data in shared memory 312 that matches therequested data, in one embodiment, operation optimization mechanism 302may load the requested data from storage system 308 via the I/O loadoperation.

In yet another embodiment, rather than loading the requested data fromstorage system 308 via the I/O load operation in response to failing toidentify untransformed data in shared memory 312 that matches therequested data, operation optimization mechanism 302 may, prior toloading the requested data from storage system 308, search transformeddata in global memory 314 to identify data that matches the requesteddata. Global memory 314 comprises data visible to all threads within allblocks associated with all of processors 304 of data processing system300.

For data to be located in global memory 314, operation optimizationmechanism 302 determines whether a portion of data in local memory 310or shared memory 312 is being utilized across processors 304 eachrunning at least one application, i.e. multiple threads in multipleblocks, in data processing system 300 above a predetermined frequencythreshold. If operation optimization mechanism 302 determines that theportion of data in local memory 310 or shared memory 312 is beingutilized across processors 304 each running at least one application indata processing system 300 above the predetermined frequency threshold,operation optimization mechanism 302 moves the portion of data fromlocal memory 310 of shared memory 312 to global memory 314, globalmemory 314 being accessible by all of processors 304. Thus, operationoptimization mechanism 302 is then able to redirect accesses to theportion of data to global memory 314.

If operation optimization mechanism 302 identities transformed data inglobal memory 314 that matches the requested data, operationoptimization mechanism 302 uses the transformed data in global memory314 to fulfill the request. If operation optimization mechanism 302fails to identify transformed data in global memory 314 that matches therequested data, operation optimization mechanism 302 searchesuntransformed data in global memory 314 to identify data that matchesthe requested data. If operation optimization mechanism 302 identifiesuntransformed data in global memory 314 that matches the requested data,operation optimization mechanism 302 uses the untransformed data inglobal memory 314 to fulfill the request. If operation optimizationmechanism 302 fails to identify untransformed data in global memory 314that matches the requested data, operation optimization mechanism 302loads the requested data from storage system 308 via the I/O loadoperation.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

FIGS. 4A and 4B depict an exemplary flowchart of the operationsperformed by an operation optimization mechanism that optimizes I/O loadoperations in accordance with an illustrative embodiment. As theoperation begins, the operation optimization mechanism receives arequest from an application via a processor to load data from a storagesystem via an input/output (I/O) load operation (step 402). Prior toloading the requested data via the I/O load operation from the storagesystem, the operation optimization mechanism searches transformed datain the local memory to identify data that matches the requested data(step 404). If at step 404 the operation optimization mechanismidentifies transformed data in local memory that matches the requesteddata, the operation optimization mechanism uses the transformed data inthe local memory to fulfill the request (step 406), with the operationending thereafter. If at step 404 the operation optimization mechanismfails to identify transformed data in the local memory that matches therequested data, the operation optimization mechanism searchesuntransformed data in the local memory to identify data that matches therequested data (step 408). If at step 408 the operation optimizationmechanism identifies untransformed data in the local memory that matchesthe requested data, the operation optimization mechanism uses theuntransformed data in the local memory to fulfill the request (step410), with the operation ending thereafter.

If at step 408 the operation optimization mechanism fails to identifyuntransformed data in the local memory that matches the requested data,the operation optimization mechanism, prior to loading the requesteddata from the storage system, searches transformed data in the sharedmemory to identify data that matches the requested data (step 412). Ifat step 412 the operation optimization mechanism identities transformeddata in the shared memory that matches the requested data, the operationoptimization mechanism uses the transformed data in the shared memory tofulfill the request (step 414), with the operation ending thereafter. Ifat step 412 the operation optimization mechanism fails to identifytransformed data in the shared memory that matches the requested data,the operation optimization mechanism searches untransformed data in theshared memory to identify data that matches the requested data (step416). If at step 416 the operation optimization mechanism identifiesuntransformed data in the shared memory that matches the requested data,the operation optimization mechanism uses the untransformed data in theshared memory to fulfill the request (step 418), with the operationending thereafter.

If at step 416 the operation optimization mechanism fails to identifyuntransformed data in the shared memory that matches the requested data,the operation optimization mechanism, prior to loading the requesteddata from the storage system, searches transformed data in the globalmemory to identify data that matches the requested data (step 420). Ifat step 420 the operation optimization mechanism identifies transformeddata in the global memory that matches the requested data, the operationoptimization mechanism uses the transformed data in the global memory tofulfill the request (step 422), with the operation ending thereafter. Ifat step 420 the operation optimization mechanism fails to identifytransformed data in the global memory that matches the requested data,the operation optimization mechanism searches untransformed data in theglobal memory to identify data that matches the requested data (step424). If at step 424 the operation optimization mechanism identifiesuntransformed data in the global memory that matches the requested data,the operation optimization mechanism uses the untransformed data in theglobal memory to fulfill the request (step 426), with the operationending thereafter. If at step 424 the operation optimization mechanismfails to identify untransformed data in the global memory that matchesthe requested data, the operation optimization mechanism loads therequested data from the storage system via the I/O load operation (step428), with the operation ending thereafter.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Thus, the illustrative embodiments provide mechanisms for an operationoptimization mechanism that, responsive to a request from an applicationto load data from a storage system via a input/output (I/O) loadoperation, prior to loading the requested data from the storage system,searches for transformed data and then untransformed data in a localmemory, shared memory, and global memory hierarchy to identify data thatmatches the requested data. If the operation optimization mechanismidentifies transformed data or untransformed data in the local memory,shared memory, and global memory, hierarchy that matches the requesteddata, the operation optimization mechanism uses the transformeddata/transformed data to fulfill the request. If the operationoptimization mechanism fails to identify transformed data/untransformeddata in the local memory, shared memory, and global memory, hierarchythat matches the requested data, only then does the operationoptimization mechanism loads the requested data from the storage systemvia the I/O load operation.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a communication bus, such as a system bus,for example. The memory elements can include local memory employedduring actual execution of the program code, bulk storage, and cachememories that provide temporary storage of at least some program code inorder to reduce the number of times code must be retrieved from bulkstorage during execution. The memory may be of various types including,but not limited to, ROM, PROM, EPROM, EEPROM, DRAM, SRAM, Flash memory,solid-state memory, and the like.

Input/output or I/O devices including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening wired or wireless I/O interfaces and/orcontrollers, or the like. I/O devices may take many different formsother than conventional keyboards, displays, pointing devices, and thelike, such as for example communication devices coupled through wired orwireless connections including, but not limited to, smart phones, tabletcomputers, touch screen devices, voice recognition devices, and thelike. Any known or later developed I/O device is intended to be withinthe scope of the illustrative embodiments.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modems and Ethernet cards are just a few of thecurrently available types of network adapters for wired communications.Wireless communication based network adapters may also be utilizedincluding, but not limited to, 802.11a/b/g/n wireless communicationadapters, Bluetooth wireless adapters, and the like. Any known or laterdeveloped network adapters are intended to be within the spirit andscope of the present invention.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method, in a data processing system comprisingat least one processor and at least one memory, the at least one memorycomprising instructions executed by the at least one processor to causethe at least one processor to implement operation optimization in thedata processing system, the method comprising: responsive to a requestfrom an application to load data from a storage system via ainput/output (I/O) load operation, prior to loading the data from thestorage system, searching, by the processor, transformed data in a localmemory to identify data that matches the requested data; responsive toidentifying transformed data in the local memory that matches therequested data, using, by the processor, the transformed data in thelocal memory to fulfill the request; responsive to failing to identifytransformed data in the local memory that matches the requested data,searching, by the processor, untransformed data in the local memory toidentify data that matches the requested data; responsive to identifyinguntransformed data in the local memory that matches the requested data,using, by the processor, the untransformed data in the local memory tofulfill the request; and responsive to failing to identify untransformeddata in the local memory that matches the requested data, loading, bythe processor, the requested data from the storage system via the I/Oload operation.
 2. The method of claim 1, further comprising: responsiveto failing to identify untransformed data in the local memory thatmatches the requested data and prior to loading the requested data fromthe storage system via the I/O load operation, searching, by theprocessor, transformed data in a shared memory to identify data thatmatches the requested data; responsive to identifying transformed datain the shared memory that matches the requested data, using, by theprocessor, the transformed data in the shared memory to fulfill therequest; responsive to failing to identify transformed data in theshared memory that matches the requested data, searching, by theprocessor, untransformed data in the shared memory to identify data thatmatches the requested data; responsive to identifying untransformed datain the shared memory that matches the requested data, using, by theprocessor, the untransformed data in the shared memory to fulfill therequest; and responsive to failing to identify untransformed data in theshared memory that matches the requested data, loading, by theprocessor, the requested data from the storage system via the I/O loadoperation.
 3. The method of claim 2, further comprising: responsive tofailing to identify untransformed data in the shared memory that matchesthe requested data and prior to loading the requested data from thestorage system via the I/O load operation, searching, by the processor,transformed data in a global memory to identify data that matches therequested data; responsive to identifying transformed data in the globalmemory that matches the requested data, using, by the processor, thetransformed data in the global memory to fulfill the request; responsiveto failing to identify transformed data in the global memory thatmatches the requested data, searching, by the processor, untransformeddata in the global memory to identify data that matches the requesteddata; responsive to identifying untransformed data in the global memorythat matches the requested data, using, by the processor, theuntransformed data in the global memory to fulfill the request; andresponsive to failing to identify untransformed data in the globalmemory that matches the requested data, loading, by the processor, therequested data from the storage system via the I/O load operation. 4.The method of claim 1, further comprising: responsive to a portion ofdata in the local memory being utilized by more than one application,marking, by the processor, the portion of data such that the portion ofdata is persisted within the local memory for a longer time thanunmarked portions of data.
 5. The method of claim 1, further comprising:responsive to a portion of data in the local memory being utilized bymultiple processing systems within a same block each running anapplication in the data processing system above a predeterminedfrequency threshold, moving, by the processor, the portion of data fromthe local memory to a shared memory, the shared memory being accessibleby the multiple processing systems; and redirecting, by the processor,accesses to the portion of data to the shared memory.
 6. The method ofclaim 1, further comprising: responsive to identifying a data stream tobe utilized by multiple processing systems within a same block eachrunning an application in the data processing system, loading, by theprocessor, the data stream into a shared memory, the shared memory beingaccessible by the multiple processing systems; and redirecting, by theprocessor, accesses to the data stream to the shared memory.
 7. Themethod of claim 4, wherein identifying the data stream to be utilized bythe multiple processing systems comprises: performing, by the processor,a static code analysis of each application running on each of themultiple processing systems to identify one or more data streams to beutilized; responsive to two or more applications utilizing a same datastream, identifying, by the processor, the data stream as the datastream to be utilized by the multiple processing systems.
 8. The methodof claim 1, further comprising: responsive to a portion of data in thelocal memory or a shared memory being utilized by multiple processingsystems within a different blocks each running an application in thedata processing system above a predetermined frequency threshold,moving, by the processor, the portion of data from the local memory orthe shared memory to a global memory, the global memory, beingaccessible all the processing systems; and redirecting, by theprocessor, accesses to the portion of data to the global memory.